Plotting output of simulations run on CSF3 on 05-08.01.24

Libraries

library(ggplot2)
library(viridis)

Loading required package: viridisLite

library(pheatmap)
library(grid)
library(gridExtra)

Data

Parameters

Directories used:

outdir <- "/mnt/bmh01-rds/UoOxford_David_W/b05055gj/AmplificationTimeR_synthetic_tests/multinomial_sampled_data/final_figures/"
results_dir <- "/mnt/bmh01-rds/UoOxford_David_W/b05055gj/AmplificationTimeR_synthetic_tests/multinomial_sampled_data/"

Experimental parameters:

state_timing_replicates <- 100 # number of replicates for error vs mut number
n_replicates_clocklike <- 100 # number of replicates for error vs clocklike
n_mutations_clocklike_test <- 100 # number of mutations used for clocklike assessment
n_replicates_mismatched <- 100 # number of replicates for timing with different equations
n_mutations_mismatched <- 100 # number of mutations used for timing with different equations
data_type <- "multinomial"

Output figure parameters:

figure_height <- 22.5
figure_width <- 17.8
figure_width_half <- 8.6
resolution <- 350

Multinomial simulated data

# Mutation number
multinomial_mutation_number_and_accuracy <- read.delim(paste0(results_dir,"Error_vs_mutation_number/mutation_number_and_error_rate_100_replicates_multinomial_sampled_data_2024-01-07.txt"), sep = "\t", header = TRUE, row.names = 1)
multinomial_mutation_number_and_number_correct_order <- read.delim(paste0(results_dir,"Error_vs_mutation_number/mutation_number_and_correct_order_100_replicates_multinomial_sampled_data_2024-01-07.txt"), sep = "\t", header = TRUE, row.names = 1)

# Clocklike proportion
multinomial_clocklike_proportions_and_accuracy <- read.delim(paste0(results_dir,"Error_vs_proportion_clocklike/clocklike_proportion_and_error_rate_100_replicates100_mutations_multinomial_sampled_data_2024-01-08.txt"), sep = "\t", header = TRUE, row.names = 1)
multinomial_clocklike_proportions_and_number_correct_order <- read.delim(paste0(results_dir,"Error_vs_proportion_clocklike/clocklike_proportion_and_correct_order_100_replicates100_mutations_multinomial_sampled_data_2024-01-08.txt"), sep = "\t", header = TRUE, row.names = 1)

Multinomial simulated data plots

Mutation Number

Error rate v mutation number

colnames(multinomial_mutation_number_and_accuracy) <- gsub("X","",colnames(multinomial_mutation_number_and_accuracy))
plot_multinomial_mutation_number_and_accuracy <- as.matrix(multinomial_mutation_number_and_accuracy)
rownames(plot_multinomial_mutation_number_and_accuracy) <- rownames(multinomial_mutation_number_and_accuracy)

png(paste0(outdir,"mutation_number_and_error_rate_",
                                                 state_timing_replicates,"_replicates",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_mutation_number_and_accuracy, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        # col = viridis(100),
        color = c(viridis(100), viridis(100)[100]),
        breaks = c(0:100,max(plot_multinomial_mutation_number_and_accuracy)),
        main = paste0("Average error rate (%) of timing (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
        xlab = "Number of mutations simulated",
        ylab = "Copy number state and order",
        border_color = NA,
        scale = "none",
        fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))

dev.off()

png 
  3 

Correct orders v mutation number

colnames(multinomial_mutation_number_and_number_correct_order) <- gsub("X","",colnames(multinomial_mutation_number_and_number_correct_order))
plot_multinomial_mutation_number_and_number_correct_order <- as.matrix(multinomial_mutation_number_and_number_correct_order)
rownames(plot_multinomial_mutation_number_and_number_correct_order) <- rownames(multinomial_mutation_number_and_number_correct_order)

png(paste0(outdir,
                                                 "mutation_number_and_correct_order_",
                                                 state_timing_replicates,"_replicates",
                                                 "_","multinomial_Data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_mutation_number_and_number_correct_order, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        col = viridis(100),
        main = paste0("Number of correct timing orders (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
        border_color = NA,
        scale = "none",
        fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))

dev.off()

png 
  3 

Clocklike proportion

Error rate v clocklike proportion

colnames(multinomial_clocklike_proportions_and_accuracy) <- gsub("X","",colnames(multinomial_clocklike_proportions_and_accuracy))
plot_multinomial_clocklike_proportions_and_accuracy <- as.matrix(multinomial_clocklike_proportions_and_accuracy)
rownames(plot_multinomial_clocklike_proportions_and_accuracy) <- rownames(multinomial_clocklike_proportions_and_accuracy)

png(paste0(outdir,
                                                 "clocklike_proportion_and_error_rate_",
                                                 n_replicates_clocklike,"_replicates",
                                                 n_mutations_clocklike_test,"_mutations",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_clocklike_proportions_and_accuracy, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        # col = viridis(100),
        color = c(viridis(100), viridis(100)[100]),
        breaks = c(0:100,max(plot_multinomial_clocklike_proportions_and_accuracy)),
        main = paste0("Average error rate (%) of timing (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
        xlab = "Proportion of clocklike mutations simulated",
        ylab = "Copy number state and order",
        border_color = NA,
        scale = "none",
        fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Proportion of clocklike mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))

dev.off()

png 
  3 

Correct orders v clocklike proportion

colnames(multinomial_clocklike_proportions_and_number_correct_order) <- gsub("X","",colnames(multinomial_clocklike_proportions_and_number_correct_order))
plot_multinomial_clocklike_proportions_and_number_correct_order <- as.matrix(multinomial_clocklike_proportions_and_number_correct_order)
rownames(plot_multinomial_clocklike_proportions_and_number_correct_order) <- rownames(multinomial_clocklike_proportions_and_number_correct_order)

png(paste0(outdir,
                                                 "clocklike_proportion_and_correct_order_",
                                                 n_replicates_clocklike,"_replicates",
                                                 n_mutations_clocklike_test,"_mutations",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, res = resolution, units = "cm")
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.95, name="vp", just=c("right","top"))), action="prepend")
pheatmap(plot_multinomial_clocklike_proportions_and_number_correct_order, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        col = viridis(100),
        main = paste0("Number of correct timing orders (n=",state_timing_replicates,") - ","Multinomial Simulated Data"),
        border_color = NA,
        scale = "none",
        fontsize_row = 7)
setHook("grid.newpage", NULL, "replace")
grid.text("Proportion of clocklike mutations simulated", y=-0.02, gp=gpar(fontsize=16))
grid.text("Copy number state and order", x=-0.07, rot=90, gp=gpar(fontsize=16))

dev.off()

png 
  3 

Combined plot

Plotting two error rateheatmaps together

mut_number_accuracy_heatmap <- pheatmap(plot_multinomial_mutation_number_and_accuracy, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        # col = viridis(100),
        color = c(viridis(100), viridis(100)[100]),
        breaks = c(0:100,max(plot_multinomial_mutation_number_and_accuracy)),
        main = paste0("Average error rate (%) of timing \n(n=",state_timing_replicates,")"),
        xlab = "Number of mutations simulated",
        ylab = "Copy number state and order",
        border_color = NA,
        scale = "none",
        fontsize = 7)

clocklike_accuracy_heatmap <- pheatmap(plot_multinomial_clocklike_proportions_and_accuracy, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        # col = viridis(100),
        color = c(viridis(100), viridis(100)[100]),
        breaks = c(0:100,max(plot_multinomial_clocklike_proportions_and_accuracy)),
        main = paste0("Average error rate (%) of timing \n(n=",state_timing_replicates,")"),
        xlab = "Proportion of clocklike mutations simulated",
        ylab = "Copy number state and order",
        border_color = NA,
        scale = "none",
        fontsize = 7)

png(paste0(outdir,"mutation_number_and_error_rate_",
                                                 state_timing_replicates,"_replicates",
           "clocklike_proportion_and_error_rate_",
                                                 n_replicates_clocklike,"_replicates",
                                                 n_mutations_clocklike_test,"_mutations",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, res = resolution, units = "cm")

setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.95, height=0.95, name="vp", just=c("right","top"))), action="prepend")
grid.arrange(grobs = list(mut_number_accuracy_heatmap[[4]],clocklike_accuracy_heatmap[[4]]),
             ncol = 2)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, x = 0.22, gp=gpar(fontsize=12))
grid.text("Proportion of clocklike mutations simulated", y=-0.02, x = 0.73, gp=gpar(fontsize=12))
grid.text("Copy number state and order", x=-0.02, rot=90, gp=gpar(fontsize=14))

grid.text("A", x=-0.02, y=0.98, gp=gpar(fontsize=16))
grid.text("B", x=0.47, y=0.98, gp=gpar(fontsize=16))

dev.off()

png 
  2 

Plotting two order heatmaps together

mut_number_order_heatmap <- pheatmap(plot_multinomial_mutation_number_and_number_correct_order, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        col = viridis(100),
        main = paste0("Number of correct timing orders \n(n=",state_timing_replicates,")"),
        border_color = NA,
        scale = "none",
        fontsize = 7)

clocklike_order_heatmap <- pheatmap(plot_multinomial_clocklike_proportions_and_number_correct_order, 
        cluster_rows = FALSE, cluster_cols = FALSE,
        col = viridis(100),
        main = paste0("Number of correct timing orders \n(n=",state_timing_replicates,")"),
        border_color = NA,
        scale = "none",
        fontsize = 7)

png(paste0(outdir,"mutation_number_and_correct_order_",
                                                 state_timing_replicates,"_replicates",
           "clocklike_proportion_and_correct_order_",
                                                 n_replicates_clocklike,"_replicates",
                                                 n_mutations_clocklike_test,"_mutations",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, res = resolution, units = "cm")

setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.95, height=0.95, name="vp", just=c("right","top"))), action="prepend")
grid.arrange(grobs = list(mut_number_order_heatmap[[4]],clocklike_order_heatmap[[4]]),
             ncol = 2)
setHook("grid.newpage", NULL, "replace")
grid.text("Number of mutations simulated", y=-0.02, x = 0.22, gp=gpar(fontsize=12))
grid.text("Proportion of clocklike mutations simulated", y=-0.02, x = 0.73, gp=gpar(fontsize=12))
grid.text("Copy number state and order", x=-0.02, rot=90, gp=gpar(fontsize=14))

grid.text("A", x=-0.02, y=0.98, gp=gpar(fontsize=16))
grid.text("B", x=0.47, y=0.98, gp=gpar(fontsize=16))

dev.off()

png 
  2 

Mismatched equation data

Try new plotting approach

4+x

Read in sample data.

cn_4_0_WGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+0 WGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_0_GW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+0 GW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_4_1_WGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+1 WGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_1_GW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+1 GW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_4_2_WGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+2 WGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_4_2_GW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_4+2 GW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_4_x <- rbind(cn_4_0_WGG, cn_4_0_GW,
                cn_4_1_WGG, cn_4_1_GW,
                cn_4_2_WGG, cn_4_2_GW)

cn_4_x$Significance <- ifelse(cn_4_x$Spearman_adjusted_p < 0.05, "Significant","Not \nSignificant")

cn_4_x_pearson_plot <- ggplot(cn_4_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","\nNot Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.

cn_4_x_pearson_plot

Warning: Removed 3 rows containing missing values (`geom_text()`).

5+x

Read in sample data.

cn_5_0_WGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+0 WGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_0_GWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+0 GWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_5_1_WGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+1 WGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_1_GWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+1 GWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_5_2_WGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+2 WGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_5_2_GWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_5+2 GWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_5_x <- rbind(cn_5_0_WGGG, cn_5_0_GWG,
                cn_5_1_WGGG, cn_5_1_GWG,
                cn_5_2_WGGG, cn_5_2_GWG)

cn_5_x$Significance <- ifelse(cn_5_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")

cn_5_x_pearson_plot <- ggplot(cn_5_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")
cn_5_x_pearson_plot

Warning: Removed 3 rows containing missing values (`geom_text()`).

6+x

Read in sample data.

cn_6_0_WGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+0 WGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_0_GWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+0 GWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_0_GGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+0 GGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_6_1_WGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+1 WGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_1_GWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+1 GWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_1_GGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+1 GGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_6_2_WGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+2 WGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_2_GWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+2 GWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_6_2_GGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_6+2 GGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_6_x <- rbind(cn_6_0_WGGGG, cn_6_0_GWGG, cn_6_0_GGW,
                cn_6_1_WGGGG, cn_6_1_GWGG, cn_6_1_GGW,
                cn_6_2_WGGGG, cn_6_2_GWGG, cn_6_2_GGW)

cn_6_x$Significance <- ifelse(cn_6_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")

cn_6_x_pearson_plot <- ggplot(cn_6_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")
cn_6_x_pearson_plot

Warning: Removed 12 rows containing missing values (`geom_text()`).

7+x

Read in sample data.

cn_7_0_WGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+0 WGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_0_GWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+0 GWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_0_GGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+0 GGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_7_1_WGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+1 WGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_1_GWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+1 GWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_1_GGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+1 GGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_7_2_WGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+2 WGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_2_GWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+2 GWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_7_2_GGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_7+2 GGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_7_x <- rbind(cn_7_0_WGGGGG, cn_7_0_GWGGG, cn_7_0_GGWG,
                cn_7_1_WGGGGG, cn_7_1_GWGGG, cn_7_1_GGWG,
                cn_7_2_WGGGGG, cn_7_2_GWGGG, cn_7_2_GGWG)

cn_7_x$Significance <- ifelse(cn_7_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")

cn_7_x_pearson_plot <- ggplot(cn_7_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")
cn_7_x_pearson_plot

Warning: Removed 12 rows containing missing values (`geom_text()`).

8+x

Read in sample data.

cn_8_0_WGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 WGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_0_GWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 GWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_0_GGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 GGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_0_GGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+0 GGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_8_1_WGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 WGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_GWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 GWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_GGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 GGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_1_GGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+1 GGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_8_2_WGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 WGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_GWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 GWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_GGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 GGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_8_2_GGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_8+2 GGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_8_x <- rbind(cn_8_0_WGGGGGG, cn_8_0_GWGGGG, cn_8_0_GGWGG, cn_8_0_GGGW,
                cn_8_1_WGGGGGG, cn_8_1_GWGGGG, cn_8_1_GGWGG, cn_8_1_GGGW,
                cn_8_2_WGGGGGG, cn_8_2_GWGGGG, cn_8_2_GGWGG, cn_8_2_GGGW)

cn_8_x$Significance <- ifelse(cn_8_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")

cn_8_x_pearson_plot <- ggplot(cn_8_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")
cn_8_x_pearson_plot

Warning: Removed 30 rows containing missing values (`geom_text()`).

9+x

Read in sample data.

cn_9_0_WGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 WGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_0_GWGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 GWGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_0_GGWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 GGWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_0_GGGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+0 GGGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_9_1_WGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 WGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_GWGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 GWGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_GGWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 GGWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_1_GGGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+1 GGGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_9_2_WGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 WGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_GWGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 GWGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_GGWGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 GGWGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_9_2_GGGWG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_9+2 GGGWG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_9_x <- rbind(cn_9_0_WGGGGGGG, cn_9_0_GWGGGGG, cn_9_0_GGWGGG, cn_9_0_GGGWG,
                cn_9_1_WGGGGGGG, cn_9_1_GWGGGGG, cn_9_1_GGWGGG, cn_9_1_GGGWG,
                cn_9_2_WGGGGGGG, cn_9_2_GWGGGGG, cn_9_2_GGWGGG, cn_9_2_GGGWG)

cn_9_x$Significance <- ifelse(cn_9_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")

cn_9_x_pearson_plot <- ggplot(cn_9_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")
cn_9_x_pearson_plot

Warning: Removed 30 rows containing missing values (`geom_text()`).

10+x

Read in sample data.

cn_10_0_WGGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 WGGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GWGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GWGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GGWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GGWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GGGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GGGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_0_GGGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+0 GGGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_10_1_WGGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 WGGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GWGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GWGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GGWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GGWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GGGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GGGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_1_GGGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+1 GGGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_10_2_WGGGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 WGGGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GWGGGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GWGGGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GGWGGGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GGWGGGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GGGWGG <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GGGWGG_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")
cn_10_2_GGGGW <- read.delim(paste0(results_dir,"applying_right_and_wrong_equations/right_vs_wrong_equations_spearman_correlation_10+2 GGGGW_100_replicates100_mutations_multinomial_data_2024-01-05.txt"), header = TRUE, sep = "\t")

cn_10_x <- rbind(cn_10_0_WGGGGGGGG, cn_10_0_GWGGGGGG, cn_10_0_GGWGGGG, cn_10_0_GGGWGG, cn_10_0_GGGGW,
                 cn_10_1_WGGGGGGGG, cn_10_1_GWGGGGGG, cn_10_1_GGWGGGG, cn_10_1_GGGWGG, cn_10_1_GGGGW,
                 cn_10_2_WGGGGGGGG, cn_10_2_GWGGGGGG, cn_10_2_GGWGGGG, cn_10_2_GGGWGG, cn_10_2_GGGGW)

cn_10_x$Significance <- ifelse(cn_10_x$Spearman_adjusted_p < 0.05, "Significant","Not Significant")

cn_10_x_pearson_plot <- ggplot(cn_10_x, aes(x = True_order, y = Applied_order, fill = Spearman_rho))+
  geom_tile(aes(color=as.factor(Significance), width=0.9, height=0.9), size=0.3)+
  scale_colour_manual(values = c("firebrick","royalblue","grey"), limits = c("Significant","Not Significant",NA), name = "Adjusted p")+
  geom_text(aes(label=round(Spearman_rho,digits = 2)), size = 2)+
  facet_grid(rows = vars(Timepoint), cols = vars(CN_state))+
  # scale_fill_gradientn(colors = viridis_pal(option = "magma")(100), limits=c(-1, 1), 
  #                      na.value = "grey")+
  scale_fill_distiller(palette = "RdBu", limits = c(-1,1), name = "Spearman ρ")+
  # scale_fill_viridis(option = "magma")+
  theme_minimal()+
  ylab("Applied timing order")+
  xlab("True simulated timing order")
cn_10_x_pearson_plot

Warning: Removed 60 rows containing missing values (`geom_text()`).

Combined plot

png(paste0(outdir,"pearson_and_mix_match_equations_4_9_",
                                                 n_replicates_mismatched,"_replicates",
                                                 n_mutations_mismatched,"_mutations",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = figure_height, width = figure_width, units = "cm",
    res = 350)

grid.arrange(cn_4_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0.2), "cm"), legend.position = "left", strip.text = element_text(size = 8), legend.box.spacing = unit(0.05, "cm"), legend.key.width = unit(0.3,"cm"), legend.text = element_text(size = 6), legend.title = element_text(size = 6), legend.box = "horizontal", legend.spacing = unit(0,"cm")), 
             cn_5_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8)),
             cn_6_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -20)), 
             cn_7_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -20)),
             cn_8_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -25)), 
             cn_9_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 6), plot.margin = unit(c(0,0,0,0), "cm"), legend.position = "none", strip.text = element_text(size = 8), axis.text.x = element_text(angle = -25)),
             # cn_10_x_pearson_plot+theme(axis.title = element_blank(),axis.text = element_text(size = 4), plot.margin = unit(c(0,0,0,0), "cm")),
             ncol = 2, nrow = 3,
             left = "Applied timing order", bottom = "True simulated timing order",
             layout_matrix = rbind(c(1,2),
                                   c(3,4),
                                   c(5,6)
                                   # c(7,7)
                                   ),
             heights = c(3,5,9), padding = unit(0.5, "cm"))

Warning: Removed 3 rows containing missing values (`geom_text()`).
Removed 3 rows containing missing values (`geom_text()`).

Warning: Removed 12 rows containing missing values (`geom_text()`).
Removed 12 rows containing missing values (`geom_text()`).

Warning: Removed 30 rows containing missing values (`geom_text()`).
Removed 30 rows containing missing values (`geom_text()`).

grid.text("A", x=0.05, y=0.98, gp=gpar(fontsize=16))
grid.text("B", x=0.50, y=0.98, gp=gpar(fontsize=16))
grid.text("C", x=0.05, y=0.82, gp=gpar(fontsize=16))
grid.text("D", x=0.50, y=0.82, gp=gpar(fontsize=16))
grid.text("E", x=0.05, y=0.54, gp=gpar(fontsize=16))
grid.text("F", x=0.50, y=0.54, gp=gpar(fontsize=16))

dev.off()

png 
  2 

png(paste0(outdir,"pearson_and_mix_match_equations_10_X_",
                                                 n_replicates_mismatched,"_replicates",
                                                 n_mutations_mismatched,"_mutations",
                                                 "_","multinomial_data","_",
                                                 Sys.Date(),".png"),
    height = 16, width = figure_width, units = "cm",
    res = 350)
cn_10_x_pearson_plot+
  theme(axis.text = element_text(size = 8), 
        axis.text.x = element_text(angle = -20, hjust = -0.01))

Warning: Removed 60 rows containing missing values (`geom_text()`).

dev.off()

png 
  2 

Session info

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS:   /opt/apps/apps/gcc/R/4.2.2/lib64/R/lib/libRblas.so
LAPACK: /opt/apps/apps/gcc/R/4.2.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] gridExtra_2.3     pheatmap_1.0.12   viridis_0.6.4     viridisLite_0.4.2
[5] ggplot2_3.4.4    

loaded via a namespace (and not attached):
 [1] RColorBrewer_1.1-3 bslib_0.4.1        compiler_4.2.2     pillar_1.9.0      
 [5] jquerylib_0.1.4    tools_4.2.2        digest_0.6.33      jsonlite_1.8.7    
 [9] evaluate_0.22      lifecycle_1.0.3    tibble_3.2.1       gtable_0.3.4      
[13] pkgconfig_2.0.3    rlang_1.1.1        cli_3.6.1          rstudioapi_0.15.0 
[17] yaml_2.3.7         xfun_0.40          fastmap_1.1.0      withr_2.5.1       
[21] dplyr_1.1.3        knitr_1.44         generics_0.1.3     vctrs_0.6.4       
[25] sass_0.4.4         tidyselect_1.2.0   glue_1.6.2         R6_2.5.1          
[29] fansi_1.0.5        rmarkdown_2.25     farver_2.1.1       magrittr_2.0.3    
[33] scales_1.2.1       htmltools_0.5.3    colorspace_2.1-0   labeling_0.4.3    
[37] utf8_1.2.4         munsell_0.5.0      cachem_1.0.6       crayon_1.5.2      

Save session info

writeLines(capture.output(sessionInfo()), paste0(outdir,data_type,"_plotting_simulation_sessionInfo_",Sys.Date(),".txt"))